Crowdsourcing the vote: New horizons in citizen forecasting

Mickael Temporão, Yannick Dufresne, Justin Savoie and Clifton van der Linden

International Journal of Forecasting, 2019, vol. 35, issue 1, 1-10

Abstract: People do not know much about politics. This is one of the most robust findings in political science and is backed by decades of research. Most of this research has focused on people’s ability to know about political issues and party positions on these issues. But can people predict elections? Our research uses a very large dataset (n>2,000,000) collected during ten provincial and federal elections in Canada to test whether people can predict the electoral victor and the closeness of the race in their district throughout the campaign. The results show that they can. This paper also contributes to the emerging literature on citizen forecasting by developing a scaling method that allows us to compare the closeness of races and that can be applied to multiparty contexts with varying numbers of parties. Finally, we assess the accuracy of citizen forecasting in Canada when compared to voter expectations weighted by past votes and political competency.

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:35:y:2019:i:1:p:1-10

DOI: 10.1016/j.ijforecast.2018.07.011

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